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Route leaks pose a significant threat to the Internet, yet traditional machine learning-based detection models often fail to accurately identify the responsible AS, hindering timely alerting. To address this, we introduce LeakFocus, a novel ...

Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the Local Path Planning (LPP) problem. However, its practical application remains significantly constrained due to its limited training efficiency and generalization ...

Wireless Power Transfer technology, which can charge IoT devices over the air, has become a promising technology for IoT networks. In wireless-powered IoT networks, broadcasting is a fundamental networking service for disseminating messages to the whole ...

To explore the prediction accuracy of tool wear and achieve model parameter adaptive fine-tuning of a tool wear monitoring model used under different sensor combinations, this paper develops a tool wear state recognition model of deep forest (DF) ...

• A tool wear recognition framework is developed using adaptive fine-tuning deep learning for variable sensor combinations.
• The excellent feature selection ability of DF is systematically analyzed and verified.
• DF is validated for ...

• A mixed-integer programming formulation for VRPOR with multiple type of products and time windows is presented.
• The problem considers split delivery and split integer constraint.
• A modified ALNS method based on the framework ALNS ...

This paper addresses the capacitated vehicle routing problem for omnichannel retailing with multiple types of time windows (hard time windows and soft time windows) and multiple types of products simultaneously. The problem aims to transfer ...

With the rapid development of artificial intelligence and deep learning, particularly with the emergence of pre-trained large models, the tremendous pressure on computational power has led to an increasing demand for hardware accelerators. This trend ...

Multi-agent trajectory prediction is crucial for many real-world applications. This task faces challenges in effectively capturing individual temporal patterns and complex interactions between intelligent agents. Existing models either solely ...

This study explores the bipartite secure synchronization problem of coupled quaternion-valued neural networks (QVNNs), in which variable sampled communications and random deception attacks are considered. Firstly, by employing the signed graph ...

The continuous development of optical remote sensing images has provided a valuable data source for object detection samples. Labelling these samples for deep learning research in optical remote sensing is crucial yet time-consuming. Traditional ...

• Offline iteration-based real-time hybrid simulation (OI-RTHS) is proposed.
• OI-RTHS offers a solution for fluid–structure dynamic interaction (FSDI) problem.
• High-fidelity FSDI effects in seismic dynamic tests is realized.
• ...

This study introduces an offline iteration-based real-time hybrid simulation (OI-RTHS) method, a novel approach for simulating fluid–structure dynamic interaction (FSDI) under seismic excitation. With this method, hydrodynamic forces are treated ...

Even the artificial intelligence (AI) has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the ...

With the rapid development of sixth-generation (6G) communication technology, global communication networks are moving towards the goal of comprehensive and seamless coverage. In particular, low earth orbit (LEO) satellites have become a critical ...

Global airport perception levels vary due to natural geographical factors and economic development disparities. Understanding these differences is crucial for assessing regional airport development and its correlation with geographical patterns. ...

• A unified model assesses airport perceived difficulty globally.
• Main factors affecting regional salience differences are identified.
• Factors are quantified for various downstream target calculation frameworks.

Accurately predicting traffic conditions is vital for smart city development, yet it remains challenging due to the intricate spatio-temporal dependencies in road networks. Existing works often propose intra-mixing deep learning-based prediction models ...

The edge intelligence (EI) has been widely applied recently. Splitting the model between device, edge server, and cloud can significantly improve the performance of EI. The model segmentation without user mobility has been investigated in detail in ...

Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, ...

Mainstream deep learning models do not perform well in skin lesion image classification for skin disease diagnosis due to multiple factors, such as noise and illumination intensity variations of skin lesion images, similarity of lesion features, ...

Infrared small target detection has significant application value in military, security, medical and other fields. However, due to the phenomenon of fuzzy edge contours of infrared small targets during imaging, coupled with interference from ...

Transformer attention plays an important role in current top-performing trackers. However, it is bottom-up, driven by stimulus and lacks intrinsic prior guidance. This bottom-up attention mechanism leads to an emphasis on all objects in the input images, ...

Recently, RGB-T tracking methods have made significant progress, demonstrating remarkable capabilities in addressing the complexities of tracking tasks within demanding environments. However, these methods overlook instability of modal validity in real-...